How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction

Human Active Learning

Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges

Multi-Source Neural Variational Inference

Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source's posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence (AAAI) 201

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks

Promoting Persuasion Knowledge in Third and Fourth Graders Through Advertising Literacy and Argumentation Interventions

The goal of this study was to promote the development of persuasion knowledge in third and fourth graders by examining children’s interpretation and production of persuasive messages through an instructional intervention. Two interventions were delivered to students that focused on the skills associated with critical thinking (e.g., evaluating effectiveness of arguments, writing a persuasive argument using valid reasoning, and understanding the persuasive intentions and tactics of advertisements). One intervention used advertising as the instructional tool, such that students were taught about the purpose of advertising, advertising tactics, and the companies and advertisers behind the ads. Students learned that ads are created to persuade people to think or do something. Additionally, students learned to ask questions about what information may be missing from the ad. A separate group of students participated in the Argumentation Intervention, which taught the basic components of an argument and the concept of biases. Students were taught the importance of using compelling evidence to support their side of a topic and how others’ perspectives must be acknowledged when developing an effective argument. Both studies assessed the same areas to examine the scope of each intervention. Measures of children’s conceptual advertising knowledge and attitudes toward advertising in a pre-posttest design were used to identify changes in these areas. Students also participated in tasks that measured changes in their ability to evaluate argumentative messages and develop a written persuasive argument. These activities measured their use of tactics to create a persuasive argument and their ability to identify the more effective argument. Beyond improving their written persuasive arguments, participants in the Argumentation Intervention significantly increased their understanding of selling intentions and understanding of persuasive tactics used in advertising . Those in the Advertising Literacy Intervention showed a significant improvement on their inclusion of others’ perspectives when writing a persuasive argument in addition to making gains to their understanding of selling and persuasive intent and skepticism toward advertising. The ability of participants in both interventions to generalize what was explicitly taught to new domains is encouraging for educators who aim to instill critical thinking skills in students. The current study provides important insights into effective instructional strategies for increasing children’s understanding and application of persuasion knowledge in everyday contexts

Parental Help-seeking for Pediatric Insomnia: Where, When, and Why Do Parents Seek Help?

Pediatric insomnia affects approximately 25% of children and can impact both child and parent functioning. Despite its prevalence and impact, next-to-nothing is known about help-seeking mechanism for pediatric insomnia. These mechanisms are expected to mirror models from the children’s mental health help-seeking literature. Both studies in this thesis gathered data from an online multinational project. Manuscript 1 investigated the informal, informational, and formal sources of help for pediatric insomnia and the factors that motivated parents to seek professional help. Parents utilized or expected to utilize a variety of informal (most commonly their partner, friends, or family members) and informational (most commonly the internet and books) help sources. Further, parents were most likely to begin formal help seeking with a primary care provider. Most parents reported child behavioural problems and the impact on their own daytime functioning as the main reason for seeking help. Manuscript 2 identified (1) predictors of problem perception and help seeking, (2) reasons why parents did not seek help, and (3) factors that differentiated parents who did and did not seek help. Sleep problem severity and child mental health problems were significant predictors of parents perceiving pediatric insomnia; whereas parental mental health problems were a significant predictor of seeking professional help. Parents who perceived a moderate-to-severe sleep problem were most often impeded from help-seeking by logistic barriers (e.g., treatment too expensive). Help-seeking and non-help-seeking parents were differentiated by sleep problem severity, and child and parent mental health problems. The results of this thesis can be used to inform the design and applicability of interventions for pediatric insomnia and in the design of a model of care for pediatric insomnia